Evolution of the representation of global vegetation by vegetation continuous fields

Two decades of global annual fractional vegetation cover products have been derived using daily surface reflectance and land surface temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA's Terra satellite. These MODIS Vegetation Continuous Fields (VCF) products are unique and a distinct advance over discrete (ordinal) land cover characterizations. VCF consists of three continuous fractional cover layers at 250 m resolution summing to 100%: tree cover, non-tree vegetation and non-vegetated surface plus surface water. This paper describes the fully-automated machine learning algorithm used to create MODIS VCF and details recent algorithm improvements. Comparisons of the latest VCF products in the MOD44B collection 6, with field-based fractional tree cover and other remote sensing fractional cover products demonstrate improved agreement: the mot mean square (RMS) deviations range from 9 to 23%. A comparison of MODIS canopy cover values with those from the Global Ecosystem Dynamics Investigation (GEDI) gives an R-2 value of 0.53. Automation of the algorithm provides flexibility for readily porting the method to derive fractional cover products from additional instruments with similar radiometric characteristics such as the Visible Infrared Imaging Radiometer (VIIRS) payload aboard Suomi NPP and JPSS platforms, and successors.

Automated summary

This paper discusses the unique algorithm used to derive global annual fractional vegetation cover products from MODIS instrument data over two decades, showing improved agreement with field-based and other remote sensing methods.